Finally, the complex structure of the model warrants scalable estimation approaches. Toward this end, as an alternative to computationally expensive Markov chain Monte Carlo (MCMC) sampling techniques, we employ an Expectation–Maximization (EM)-based estimation strategy [1]}, where we only search for the posterior mode to iteratively estimate the posterior selection probabilities. Our EM-based estimation is an generalization of [1]} to a multiple-multivariate regression setting, and provides a scalable alternative to sampling strategies; it also facilitates model selection. While developed in the context of this specific imaging-genomics case study, our sequential modeling framework is general, has independent methodological value and could also be used in other applications, which exhibit a natural ordering, e.g. multivariate data collected over time, space, or other axes.
